Data & Analytics

Brain foundation models (BFMs) variance allocation problem methodology — third-order statistics (co-skewness) predict cognition where billion-parameter models fail.

RE-CONFIRM framework for validating robustness of biomarkers discovered by brain foundation models from dynamic functional connectivity. Systematic evaluation of internal reliability, external reliability, and validity for clinical biomarkers. Activation: RE-CONFIRM, biomarker validation, brain foundation model, robust biomarkers, dynamic functional connectivity.

Mathematical framework for quantifying the value of brain data for machine learning. Derives scaling laws, exchange rates between brain and task samples, and conditions for robustness gains via neural regularization. Activation: brain data value, neural data worth, brain-regularized learning, neuroai scaling laws, brain sample exchange rate.

Comparative methodology for brain alignment across learning rules (BP, FA, PC, STDP). Key finding: single training epoch reduces V1 alignment by 25-90%. BP most destructive, PC and STDP preserve brain-like structure. Use when: brain alignment, representational similarity analysis, biologically plausible learning, visual cortex modeling, learning rule comparison. arXiv: 2605.30556

**arXiv ID:** 2505.06257 **Authors:** Ahsan Adeel **Published:** 2025-05-02T14:31:10Z **Abstract:** Attending to what is relevant is fundamental to both the mammalian brain and modern machine learning models such as Transformers. Yet, determining relevance remains a core challenge, traditionally offloaded to learning algorithms like backpropagation. Inspired by recent cellular neurobiological evidence linking neocortical pyramidal cells to distinct mental states, this work shows how models (e...

BCI-sift (BCI Systematic and Interpretable Feature Tuning) methodology for automated feature selection in Brain-Computer Interface applications. Integrates advanced optimization algorithms (scikit-learn compatible) to identify informative neural features across electrode, temporal, and frequency dimensions from HD ECoG and other BCI modalities. Activates on BCI feature selection, ECoG decoding optimization, neural feature tuning, automated BCI ML pipeline, brain-computer interface classificat...

Bayesian dynamical framework for modeling time-order effects in sequential haptic perception. Captures perceptual biases from prior expectations and temporal structure using drift-diffusion dynamics. Activation: haptic perception, Bayesian dynamics, time-order effects, sequential stimuli, perceptual bias.

Interpretable EEG biomarkers with bag-of-waves: Spatial and temporal waveform dictionaries for low-data regimes. Use when analyzing EEG data in low-data scenarios, needing interpretable biomarkers, or working with clinical EEG classification. Activation: bag-of-waves, EEG biomarkers, interpretable EEG, waveform dictionaries, low-data EEG

Two-stage interpretation of attention as in-context empirical Bayes inference via particle dynamics with posterior mean recovery guarantees

**arXiv ID:** 2005.09512 **Authors:** Leonardo Augusto Ferreira, Frederico Gadelha Guimarães, Rodrigo Silva **Published:** 2020-05-18T16:09:49Z **Abstract:** Explainable Artificial Intelligence (or xAI) has become an important research topic in the fields of Machine Learning and Deep Learning. In this paper, we propose a Genetic Programming (GP) based approach, named Genetic Programming Explainer (GPX), to the problem of explaining decisions computed by AI systems. The method generates a nois...

Generalized framework of antisymmetric cross-polyspectral indices for identifying high-order neural interactions. Quantifies cross-frequency coupling while being intrinsically robust to volume conduction artifacts. Applicable to EEG/MEG analysis and personalized mTMS protocol design. Activation: antisymmetric polyspectral, cross-frequency coupling, high-order neural interactions, volume conduction robust, bispectral analysis, trispectral analysis, multi-frequency coupling, mTMS protocol.

**arXiv ID:** 2304.08488 **Authors:** Shikhar Bahl, Russell Mendonca, Lili Chen, Unnat Jain, Deepak Pathak **Published:** 2023-04-17T17:59:34Z **Abstract:** Building a robot that can understand and learn to interact by watching humans has inspired several vision problems. However, despite some successful results on static datasets, it remains unclear how current models can be used on a robot directly. In this paper, we aim to bridge this gap by leveraging videos of human interactions in an en...

**arXiv ID:** 2602.06997 **Authors:** Anindya Bhattacharjee, Nittya Ananda Biswas, K. A. Shahriar, Adib Rahman **Published:** 2026-01-28T12:14:45Z **Abstract:** Emotion recognition from physiological signals remains challenging due to their non-stationary, noisy, and subject-dependent characteristics. This work presents, to the best of our knowledge, the first comprehensive application of liquid neural networks for EEG-based emotion recognition. The proposed multimodal framework combines conv...

**arXiv ID:** 2503.22742 **Authors:** William Claster, Suhas KM, Dhairya Gundechia **Published:** 2025-03-26T19:32:31Z **Abstract:** We propose Adaptive Integrated Layered Attention (AILA), a neural network architecture that combines dense skip connections with different mechanisms for adaptive feature reuse across network layers. We evaluate AILA on three challenging tasks: price forecasting for various commodities and indices (S&P 500, Gold, US dollar Futures, Coffee, Wheat), image recognit...

Epidemic spreading with activity predicts Alzheimer's.

**arXiv ID:** 1801.00512 **Authors:** Haik Manukian, Fabio L. Traversa, Massimiliano Di Ventra **Published:** 2018-01-01T21:27:11Z **Abstract:** Restricted Boltzmann machines (RBMs) and their extensions, called 'deep-belief networks', are powerful neural networks that have found applications in the fields of machine learning and artificial intelligence. The standard way to training these models resorts to an iterative unsupervised procedure based on Gibbs sampling, called 'contrastive diverge...

**arXiv ID:** 2501.17411 **Authors:** Quan Long, Bin Wang, Bing Xue, Mengjie Zhang **Published:** 2025-01-29T04:32:36Z **Abstract:** To address the issue of interpretability in multilayer perceptrons (MLPs), Kolmogorov-Arnold Networks (KANs) are introduced in 2024. However, optimizing KAN structures is labor-intensive, typically requiring manual intervention and parameter tuning. This paper proposes GA-KAN, a genetic algorithm-based approach that automates the optimization of KANs, requiring ...

CNN + Adversarial Autoencoder (AAE) for EEG signal classification — from raw EEG to image representations, latent-space regularization, and robust brain-computer interface (BCI) decoding.

Skill for exploring brain networks using noninvasive electrophysiological measurements (EEG/MEG) based on arXiv:2607.17602v1.

Domain relevance filtering methodology for neuroscience paper selection in automated research workflows. Provides criteria for determining when arXiv papers are relevant to neuroscience, brain networks, neural dynamics, spiking neural networks, and computational neuroscience domains.

Neural Variational Quantum Linear Solver (NVQLS) - first hybrid quantum-classical operator learning framework using Legendre-Galerkin weak formulation for solving parametric PDEs. Achieves superior accuracy with theoretical computational complexity advantages under efficient state preparation. Activation: quantum operator learning, quantum PDE solver, variational quantum linear solver, VQLS, quantum spectral method, quantum Galerkin method.

Neural operator framework for data-driven discovery of stability and receptivity properties in physical systems. Activation: neural operator, stability discovery, receptivity analysis, dynamical systems.

Theory of learning high-dimensional controlled non-linear dynamical systems via neural ODEs trained with online stochastic gradient descent, solved using dynamical mean field theory. Activation: neural ode, mean field theory, dynamical systems, training dynamics, learning curves, high-dimensional limit, statistical mechanics, online SGD, ResNet theory.

Neural network quantum state (NQS) architecture for grand canonical ensemble bosonic systems. Enables variational Monte Carlo with variable particle number in Fock space. Activation: neural quantum states, grand canonical ensemble, bosonic wavefunctions, Fock space, variational Monte Carlo, NQS, quantum many-body ground state.